Overview

Dataset statistics

Number of variables22
Number of observations200000
Missing cells766339
Missing cells (%)17.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.6 MiB
Average record size in memory176.0 B

Variable types

Text2
DateTime2
Numeric15
Categorical3

Alerts

dropoff_census_tract is highly overall correlated with dropoff_community_area and 3 other fieldsHigh correlation
dropoff_community_area is highly overall correlated with dropoff_census_tract and 3 other fieldsHigh correlation
dropoff_latitude is highly overall correlated with dropoff_census_tract and 4 other fieldsHigh correlation
dropoff_location is highly overall correlated with dropoff_census_tract and 3 other fieldsHigh correlation
dropoff_longitude is highly overall correlated with dropoff_census_tract and 3 other fieldsHigh correlation
extras is highly overall correlated with trip_totalHigh correlation
fare is highly overall correlated with trip_miles and 2 other fieldsHigh correlation
payment_type is highly overall correlated with tipsHigh correlation
pickup_census_tract is highly overall correlated with pickup_community_areaHigh correlation
pickup_community_area is highly overall correlated with pickup_census_tractHigh correlation
pickup_latitude is highly overall correlated with dropoff_latitude and 1 other fieldsHigh correlation
pickup_longitude is highly overall correlated with pickup_latitudeHigh correlation
tips is highly overall correlated with payment_typeHigh correlation
trip_miles is highly overall correlated with fare and 2 other fieldsHigh correlation
trip_seconds is highly overall correlated with fare and 2 other fieldsHigh correlation
trip_total is highly overall correlated with extras and 3 other fieldsHigh correlation
payment_type is highly imbalanced (62.8%)Imbalance
pickup_census_tract has 111053 (55.5%) missing valuesMissing
dropoff_census_tract has 122048 (61.0%) missing valuesMissing
pickup_community_area has 29017 (14.5%) missing valuesMissing
dropoff_community_area has 73327 (36.7%) missing valuesMissing
tolls has 30137 (15.1%) missing valuesMissing
company has 93668 (46.8%) missing valuesMissing
pickup_latitude has 29002 (14.5%) missing valuesMissing
pickup_longitude has 29002 (14.5%) missing valuesMissing
pickup_location has 29002 (14.5%) missing valuesMissing
dropoff_latitude has 73327 (36.7%) missing valuesMissing
dropoff_longitude has 73327 (36.7%) missing valuesMissing
dropoff_location has 73327 (36.7%) missing valuesMissing
trip_seconds is highly skewed (γ1 = 21.75519908)Skewed
fare is highly skewed (γ1 = 148.025731)Skewed
tolls is highly skewed (γ1 = 125.2660433)Skewed
trip_total is highly skewed (γ1 = 124.9842732)Skewed
unique_key has unique valuesUnique
trip_seconds has 3410 (1.7%) zerosZeros
trip_miles has 12673 (6.3%) zerosZeros
tips has 137559 (68.8%) zerosZeros
tolls has 169784 (84.9%) zerosZeros
extras has 116988 (58.5%) zerosZeros

Reproduction

Analysis started2024-02-25 08:07:07.974798
Analysis finished2024-02-25 08:15:28.652520
Duration8 minutes and 20.68 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

unique_key
Text

UNIQUE 

Distinct200000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2024-02-25T08:15:29.201846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length40
Median length40
Mean length40
Min length40

Characters and Unicode

Total characters8000000
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200000 ?
Unique (%)100.0%

Sample

1st rowe2afbb4f62fb3865c4d928a39e8a4d1e711ea8da
2nd rowa663e8249660b7a3ae13c9a39378ff495564e8e6
3rd rowa99a7309aea3cf70eed1644c254eb30b150ac4f2
4th rowad0d9e702d67b0e9e7b85dd0750605ff06389c4f
5th row350b48036f17d07f79abf53d04b811da8b6c264c
ValueCountFrequency (%)
e2afbb4f62fb3865c4d928a39e8a4d1e711ea8da 1
 
< 0.1%
1cd673ed462eb277d26daef0f64acb71b5fa4ab7 1
 
< 0.1%
b7481a3988dd29243a429937ea9184c4252cd23a 1
 
< 0.1%
af14f627846d939d75957e76c4fb6cc6f94592e9 1
 
< 0.1%
a99a7309aea3cf70eed1644c254eb30b150ac4f2 1
 
< 0.1%
ad0d9e702d67b0e9e7b85dd0750605ff06389c4f 1
 
< 0.1%
350b48036f17d07f79abf53d04b811da8b6c264c 1
 
< 0.1%
b5d0ec1472abae045f794a581f42364213ef79ab 1
 
< 0.1%
ac7ce885bf43a27e0e22de0c1e8efd98ebdd1571 1
 
< 0.1%
68088a5a378c45781ece9cdb1eebdc2afc4047d3 1
 
< 0.1%
Other values (199990) 199990
> 99.9%
2024-02-25T08:15:30.360579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 501071
 
6.3%
4 500914
 
6.3%
a 500776
 
6.3%
2 500765
 
6.3%
f 500435
 
6.3%
5 500110
 
6.3%
3 500003
 
6.3%
e 499895
 
6.2%
9 499801
 
6.2%
c 499765
 
6.2%
Other values (6) 2996465
37.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000241
62.5%
Lowercase Letter 2999759
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 501071
10.0%
4 500914
10.0%
2 500765
10.0%
5 500110
10.0%
3 500003
10.0%
9 499801
10.0%
6 499612
10.0%
0 499603
10.0%
1 499443
10.0%
7 498919
10.0%
Lowercase Letter
ValueCountFrequency (%)
a 500776
16.7%
f 500435
16.7%
e 499895
16.7%
c 499765
16.7%
d 499488
16.7%
b 499400
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 5000241
62.5%
Latin 2999759
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
8 501071
10.0%
4 500914
10.0%
2 500765
10.0%
5 500110
10.0%
3 500003
10.0%
9 499801
10.0%
6 499612
10.0%
0 499603
10.0%
1 499443
10.0%
7 498919
10.0%
Latin
ValueCountFrequency (%)
a 500776
16.7%
f 500435
16.7%
e 499895
16.7%
c 499765
16.7%
d 499488
16.7%
b 499400
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8000000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 501071
 
6.3%
4 500914
 
6.3%
a 500776
 
6.3%
2 500765
 
6.3%
f 500435
 
6.3%
5 500110
 
6.3%
3 500003
 
6.3%
e 499895
 
6.2%
9 499801
 
6.2%
c 499765
 
6.2%
Other values (6) 2996465
37.5%
Distinct31737
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum2014-04-01 00:00:00+00:00
Maximum2018-07-23 15:00:00+00:00
2024-02-25T08:15:30.835866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:31.340777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct31646
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum2014-04-01 00:30:00+00:00
Maximum2018-07-23 15:00:00+00:00
2024-02-25T08:15:31.843634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:32.375441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

trip_seconds
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct4564
Distinct (%)2.3%
Missing43
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1017.3965
Minimum0
Maximum86340
Zeros3410
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-02-25T08:15:32.690593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q1420
median720
Q31200
95-th percentile2473
Maximum86340
Range86340
Interquartile range (IQR)780

Descriptive statistics

Standard deviation2151.8552
Coefficient of variation (CV)2.1150607
Kurtosis604.92366
Mean1017.3965
Median Absolute Deviation (MAD)360
Skewness21.755199
Sum2.0343554 × 108
Variance4630481
MonotonicityNot monotonic
2024-02-25T08:15:32.975064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
540 7604
 
3.8%
480 7554
 
3.8%
600 7547
 
3.8%
660 7171
 
3.6%
420 6849
 
3.4%
720 6389
 
3.2%
360 6142
 
3.1%
780 5748
 
2.9%
840 5379
 
2.7%
300 5083
 
2.5%
Other values (4554) 134491
67.2%
ValueCountFrequency (%)
0 3410
1.7%
1 3160
1.6%
2 1377
0.7%
3 771
 
0.4%
4 577
 
0.3%
5 309
 
0.2%
6 219
 
0.1%
7 219
 
0.1%
8 209
 
0.1%
9 151
 
0.1%
ValueCountFrequency (%)
86340 3
< 0.1%
85633 1
 
< 0.1%
85200 1
 
< 0.1%
84120 1
 
< 0.1%
83296 1
 
< 0.1%
82800 1
 
< 0.1%
81960 1
 
< 0.1%
80569 1
 
< 0.1%
80100 1
 
< 0.1%
79219 1
 
< 0.1%

trip_miles
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3487
Distinct (%)1.7%
Missing11
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.5430392
Minimum0
Maximum388.1
Zeros12673
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-02-25T08:15:33.272984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.3
median2.5
Q37
95-th percentile19.6
Maximum388.1
Range388.1
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation7.6250346
Coefficient of variation (CV)1.3756054
Kurtosis146.13869
Mean5.5430392
Median Absolute Deviation (MAD)1.7
Skewness6.1774545
Sum1108546.9
Variance58.141152
MonotonicityNot monotonic
2024-02-25T08:15:33.593147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12673
 
6.3%
1.7 3575
 
1.8%
1.1 3458
 
1.7%
1.2 3440
 
1.7%
1.3 3373
 
1.7%
1.5 3328
 
1.7%
1.6 3323
 
1.7%
2 3279
 
1.6%
1.8 3250
 
1.6%
1.4 3244
 
1.6%
Other values (3477) 157046
78.5%
ValueCountFrequency (%)
0 12673
6.3%
0.01 88
 
< 0.1%
0.02 51
 
< 0.1%
0.03 53
 
< 0.1%
0.04 64
 
< 0.1%
0.05 67
 
< 0.1%
0.06 83
 
< 0.1%
0.07 117
 
0.1%
0.08 95
 
< 0.1%
0.09 109
 
0.1%
ValueCountFrequency (%)
388.1 1
< 0.1%
361.6 1
< 0.1%
303.1 1
< 0.1%
293.2 1
< 0.1%
287.1 1
< 0.1%
274.5 1
< 0.1%
256.3 1
< 0.1%
250.3 1
< 0.1%
248.8 1
< 0.1%
210.36 1
< 0.1%

pickup_census_tract
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct328
Distinct (%)0.4%
Missing111053
Missing (%)55.5%
Infinite0
Infinite (%)0.0%
Mean1.7031366 × 1010
Minimum1.703101 × 1010
Maximum1.703198 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-02-25T08:15:33.917599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.703101 × 1010
5-th percentile1.7031072 × 1010
Q11.7031081 × 1010
median1.7031282 × 1010
Q31.7031831 × 1010
95-th percentile1.703198 × 1010
Maximum1.703198 × 1010
Range969898
Interquartile range (IQR)749697

Descriptive statistics

Standard deviation336604.07
Coefficient of variation (CV)1.9763774 × 10-5
Kurtosis-0.89605857
Mean1.7031366 × 1010
Median Absolute Deviation (MAD)200497
Skewness0.84754245
Sum1.5148889 × 1015
Variance1.133023 × 1011
MonotonicityNot monotonic
2024-02-25T08:15:34.246995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.70313201 × 101010377
 
5.2%
1.703198 × 10108874
 
4.4%
1.70318391 × 10108163
 
4.1%
1.70310815 × 10107072
 
3.5%
1.70313204 × 10104870
 
2.4%
1.70312819 × 10104673
 
2.3%
1.70310814 × 10104555
 
2.3%
1.70310817 × 10103862
 
1.9%
1.70313301 × 10103095
 
1.5%
1.70310814 × 10102984
 
1.5%
Other values (318) 30422
 
15.2%
(Missing) 111053
55.5%
ValueCountFrequency (%)
1.70310102 × 10101
 
< 0.1%
1.70310104 × 10103
 
< 0.1%
1.70310105 × 10101
 
< 0.1%
1.70310105 × 10101
 
< 0.1%
1.70310105 × 10102
 
< 0.1%
1.70310106 × 10101
 
< 0.1%
1.70310201 × 10101
 
< 0.1%
1.70310202 × 101035
< 0.1%
1.70310206 × 10103
 
< 0.1%
1.70310208 × 10101
 
< 0.1%
ValueCountFrequency (%)
1.70319801 × 10101903
 
1.0%
1.703198 × 10108874
4.4%
1.70318437 × 10105
 
< 0.1%
1.70318432 × 10101
 
< 0.1%
1.70318423 × 1010125
 
0.1%
1.70318422 × 1010231
 
0.1%
1.70318419 × 1010176
 
0.1%
1.70318411 × 101016
 
< 0.1%
1.7031841 × 1010585
 
0.3%
1.70318403 × 10106
 
< 0.1%

dropoff_census_tract
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct222
Distinct (%)0.3%
Missing122048
Missing (%)61.0%
Infinite0
Infinite (%)0.0%
Mean1.7031274 × 1010
Minimum1.703101 × 1010
Maximum1.703184 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-02-25T08:15:34.558868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.703101 × 1010
5-th percentile1.7031062 × 1010
Q11.7031282 × 1010
median1.7031282 × 1010
Q31.703133 × 1010
95-th percentile1.703133 × 1010
Maximum1.703184 × 1010
Range830000
Interquartile range (IQR)48200

Descriptive statistics

Standard deviation143493.56
Coefficient of variation (CV)8.4252978 × 10-6
Kurtosis5.2735237
Mean1.7031274 × 1010
Median Absolute Deviation (MAD)48200
Skewness1.3979693
Sum1.3276219 × 1015
Variance2.0590401 × 1010
MonotonicityNot monotonic
2024-02-25T08:15:34.863768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.70312819 × 101031775
 
15.9%
1.70313301 × 101026501
 
13.3%
1.70310714 × 10107071
 
3.5%
1.70310802 × 10103493
 
1.7%
1.70310619 × 10102221
 
1.1%
1.70310819 × 10101594
 
0.8%
1.70310623 × 1010888
 
0.4%
1.70310502 × 1010607
 
0.3%
1.70318094 × 1010443
 
0.2%
1.70318311 × 1010378
 
0.2%
Other values (212) 2981
 
1.5%
(Missing) 122048
61.0%
ValueCountFrequency (%)
1.70310104 × 1010127
 
0.1%
1.70310107 × 10106
 
< 0.1%
1.70310502 × 1010607
 
0.3%
1.70310618 × 1010287
 
0.1%
1.70310619 × 10102221
 
1.1%
1.70310623 × 1010888
 
0.4%
1.70310714 × 10107071
3.5%
1.70310802 × 10103493
1.7%
1.70310819 × 10101594
 
0.8%
1.70311105 × 101038
 
< 0.1%
ValueCountFrequency (%)
1.70318404 × 10101
 
< 0.1%
1.703184 × 10103
 
< 0.1%
1.70318374 × 10107
 
< 0.1%
1.70318359 × 10102
 
< 0.1%
1.70318314 × 10101
 
< 0.1%
1.70318311 × 1010378
0.2%
1.703183001 × 10105
 
< 0.1%
1.703183001 × 10102
 
< 0.1%
1.703183 × 10106
 
< 0.1%
1.703183 × 101060
 
< 0.1%

pickup_community_area
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct77
Distinct (%)< 0.1%
Missing29017
Missing (%)14.5%
Infinite0
Infinite (%)0.0%
Mean27.914453
Minimum1
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-02-25T08:15:35.174431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q18
median24
Q332
95-th percentile76
Maximum77
Range76
Interquartile range (IQR)24

Descriptive statistics

Standard deviation24.662098
Coefficient of variation (CV)0.88348847
Kurtosis-0.33557872
Mean27.914453
Median Absolute Deviation (MAD)16
Skewness0.97935535
Sum4772897
Variance608.21906
MonotonicityNot monotonic
2024-02-25T08:15:35.491255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 47925
24.0%
32 32779
16.4%
76 25155
12.6%
6 11149
 
5.6%
28 10553
 
5.3%
7 7671
 
3.8%
3 5249
 
2.6%
24 4923
 
2.5%
56 4872
 
2.4%
33 4495
 
2.2%
Other values (67) 16212
 
8.1%
(Missing) 29017
14.5%
ValueCountFrequency (%)
1 1048
 
0.5%
2 934
 
0.5%
3 5249
 
2.6%
4 1135
 
0.6%
5 1101
 
0.6%
6 11149
 
5.6%
7 7671
 
3.8%
8 47925
24.0%
9 18
 
< 0.1%
10 113
 
0.1%
ValueCountFrequency (%)
77 2564
 
1.3%
76 25155
12.6%
75 14
 
< 0.1%
74 3
 
< 0.1%
73 8
 
< 0.1%
72 3
 
< 0.1%
71 8
 
< 0.1%
70 26
 
< 0.1%
69 25
 
< 0.1%
68 10
 
< 0.1%

dropoff_community_area
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)< 0.1%
Missing73327
Missing (%)36.7%
Infinite0
Infinite (%)0.0%
Mean19.381131
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-02-25T08:15:35.800241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q18
median28
Q333
95-th percentile33
Maximum60
Range59
Interquartile range (IQR)25

Descriptive statistics

Standard deviation12.42253
Coefficient of variation (CV)0.64096002
Kurtosis-1.6406971
Mean19.381131
Median Absolute Deviation (MAD)13
Skewness0.047157273
Sum2455066
Variance154.31925
MonotonicityNot monotonic
2024-02-25T08:15:36.045504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
8 32667
16.3%
28 31775
15.9%
33 26501
 
13.3%
3 13049
 
6.5%
7 7071
 
3.5%
14 4907
 
2.5%
41 4739
 
2.4%
6 3396
 
1.7%
29 762
 
0.4%
5 607
 
0.3%
Other values (17) 1199
 
0.6%
(Missing) 73327
36.7%
ValueCountFrequency (%)
1 133
 
0.1%
3 13049
 
6.5%
5 607
 
0.3%
6 3396
 
1.7%
7 7071
 
3.5%
8 32667
16.3%
11 38
 
< 0.1%
14 4907
 
2.5%
21 378
 
0.2%
24 117
 
0.1%
ValueCountFrequency (%)
60 3
 
< 0.1%
59 1
 
< 0.1%
54 7
 
< 0.1%
52 27
 
< 0.1%
47 35
 
< 0.1%
46 155
 
0.1%
42 7
 
< 0.1%
41 4739
2.4%
38 2
 
< 0.1%
35 1
 
< 0.1%

fare
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1507
Distinct (%)0.8%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean15.666398
Minimum0
Maximum9211.59
Zeros182
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-02-25T08:15:36.355371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16.75
median10
Q318.75
95-th percentile45
Maximum9211.59
Range9211.59
Interquartile range (IQR)12

Descriptive statistics

Standard deviation41.823396
Coefficient of variation (CV)2.6696242
Kurtosis27949.116
Mean15.666398
Median Absolute Deviation (MAD)4.25
Skewness148.02573
Sum3133091.7
Variance1749.1965
MonotonicityNot monotonic
2024-02-25T08:15:36.670141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 7504
 
3.8%
2 4439
 
2.2%
3.25 3895
 
1.9%
7.25 3427
 
1.7%
8.25 3406
 
1.7%
9.25 3120
 
1.6%
6.25 2903
 
1.5%
10.25 2692
 
1.3%
8 2161
 
1.1%
5.25 2147
 
1.1%
Other values (1497) 164294
82.1%
ValueCountFrequency (%)
0 182
 
0.1%
0.01 7504
3.8%
0.07 1
 
< 0.1%
0.08 1
 
< 0.1%
0.1 2
 
< 0.1%
0.2 2
 
< 0.1%
0.27 1
 
< 0.1%
0.32 2
 
< 0.1%
0.5 1
 
< 0.1%
0.51 1
 
< 0.1%
ValueCountFrequency (%)
9211.59 1
< 0.1%
9000.9 1
< 0.1%
6300.35 1
< 0.1%
5130.63 1
< 0.1%
5004.74 1
< 0.1%
4002.78 1
< 0.1%
4001.15 1
< 0.1%
2800.17 1
< 0.1%
1081.4 1
< 0.1%
996.63 1
< 0.1%

tips
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2018
Distinct (%)1.0%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.4819713
Minimum0
Maximum199
Zeros137559
Zeros (%)68.8%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-02-25T08:15:36.954519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile8.05
Maximum199
Range199
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.4666989
Coefficient of variation (CV)2.3392483
Kurtosis169.1816
Mean1.4819713
Median Absolute Deviation (MAD)0
Skewness6.9602832
Sum296376.48
Variance12.018001
MonotonicityNot monotonic
2024-02-25T08:15:37.292124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 137559
68.8%
2 17981
 
9.0%
3 7900
 
4.0%
1 3639
 
1.8%
5 2050
 
1.0%
4 1766
 
0.9%
10 992
 
0.5%
1.5 663
 
0.3%
6 542
 
0.3%
7 526
 
0.3%
Other values (2008) 26370
 
13.2%
ValueCountFrequency (%)
0 137559
68.8%
0.01 24
 
< 0.1%
0.02 13
 
< 0.1%
0.03 5
 
< 0.1%
0.04 1
 
< 0.1%
0.05 2
 
< 0.1%
0.07 7
 
< 0.1%
0.1 27
 
< 0.1%
0.11 2
 
< 0.1%
0.15 31
 
< 0.1%
ValueCountFrequency (%)
199 1
< 0.1%
180 1
< 0.1%
155 1
< 0.1%
145 1
< 0.1%
126.05 1
< 0.1%
105 1
< 0.1%
100 1
< 0.1%
96 1
< 0.1%
75 2
< 0.1%
67 1
< 0.1%

tolls
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct23
Distinct (%)< 0.1%
Missing30137
Missing (%)15.1%
Infinite0
Infinite (%)0.0%
Mean0.0049993819
Minimum0
Maximum75
Zeros169784
Zeros (%)84.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-02-25T08:15:37.593539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum75
Range75
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.48559431
Coefficient of variation (CV)97.13087
Kurtosis16583.991
Mean0.0049993819
Median Absolute Deviation (MAD)0
Skewness125.26604
Sum849.21
Variance0.23580183
MonotonicityNot monotonic
2024-02-25T08:15:37.838722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 169784
84.9%
1.9 21
 
< 0.1%
1.5 10
 
< 0.1%
3 7
 
< 0.1%
50 7
 
< 0.1%
2 6
 
< 0.1%
4 4
 
< 0.1%
75 3
 
< 0.1%
2.1 3
 
< 0.1%
3.8 2
 
< 0.1%
Other values (13) 16
 
< 0.1%
(Missing) 30137
 
15.1%
ValueCountFrequency (%)
0 169784
84.9%
0.9 1
 
< 0.1%
1.5 10
 
< 0.1%
1.6 1
 
< 0.1%
1.9 21
 
< 0.1%
2 6
 
< 0.1%
2.1 3
 
< 0.1%
2.4 2
 
< 0.1%
2.5 1
 
< 0.1%
2.7 1
 
< 0.1%
ValueCountFrequency (%)
75 3
< 0.1%
64.5 1
 
< 0.1%
50 7
< 0.1%
28.81 1
 
< 0.1%
12.4 1
 
< 0.1%
8 1
 
< 0.1%
6 2
 
< 0.1%
5 1
 
< 0.1%
4.5 2
 
< 0.1%
4.2 1
 
< 0.1%

extras
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct246
Distinct (%)0.1%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.1075579
Minimum0
Maximum99.5
Zeros116988
Zeros (%)58.5%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-02-25T08:15:38.138521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile14
Maximum99.5
Range99.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.9408025
Coefficient of variation (CV)2.8188087
Kurtosis28.642353
Mean2.1075579
Median Absolute Deviation (MAD)0
Skewness4.7540089
Sum421486.29
Variance35.293135
MonotonicityNot monotonic
2024-02-25T08:15:38.466383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 116988
58.5%
1 32402
 
16.2%
2 11467
 
5.7%
1.5 7813
 
3.9%
4 6723
 
3.4%
3 3010
 
1.5%
5 2641
 
1.3%
6 1351
 
0.7%
2.5 1234
 
0.6%
3.5 1128
 
0.6%
Other values (236) 15231
 
7.6%
ValueCountFrequency (%)
0 116988
58.5%
0.02 1
 
< 0.1%
0.04 1
 
< 0.1%
0.1 2
 
< 0.1%
0.25 1
 
< 0.1%
0.5 615
 
0.3%
0.75 148
 
0.1%
0.9 1
 
< 0.1%
1 32402
 
16.2%
1.5 7813
 
3.9%
ValueCountFrequency (%)
99.5 6
< 0.1%
98 1
 
< 0.1%
96 1
 
< 0.1%
93.5 1
 
< 0.1%
90 3
< 0.1%
89 1
 
< 0.1%
88.5 1
 
< 0.1%
87 2
 
< 0.1%
86.5 1
 
< 0.1%
86 2
 
< 0.1%

trip_total
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5163
Distinct (%)2.6%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean19.34244
Minimum0
Maximum9299.25
Zeros179
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-02-25T08:15:38.771651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17.65
median11.5
Q321.05
95-th percentile63
Maximum9299.25
Range9299.25
Interquartile range (IQR)13.4

Descriptive statistics

Standard deviation44.482694
Coefficient of variation (CV)2.2997458
Kurtosis22245.485
Mean19.34244
Median Absolute Deviation (MAD)5
Skewness124.98427
Sum3868255.8
Variance1978.7101
MonotonicityNot monotonic
2024-02-25T08:15:39.058610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 6643
 
3.3%
2 4157
 
2.1%
3.25 3073
 
1.5%
9.25 2757
 
1.4%
10.25 2709
 
1.4%
8.25 2652
 
1.3%
7.25 2389
 
1.2%
11.25 2313
 
1.2%
12.25 1993
 
1.0%
6.25 1902
 
1.0%
Other values (5153) 169400
84.7%
ValueCountFrequency (%)
0 179
 
0.1%
0.01 6643
3.3%
0.08 1
 
< 0.1%
0.1 2
 
< 0.1%
0.2 2
 
< 0.1%
0.27 1
 
< 0.1%
0.5 1
 
< 0.1%
0.51 2
 
< 0.1%
0.52 1
 
< 0.1%
0.75 1
 
< 0.1%
ValueCountFrequency (%)
9299.25 1
< 0.1%
9001 1
< 0.1%
6300.39 1
< 0.1%
5180.63 1
< 0.1%
5057.54 1
< 0.1%
4054.58 1
< 0.1%
4001.15 1
< 0.1%
2850.19 1
< 0.1%
1081.4 1
< 0.1%
996.63 1
< 0.1%

payment_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Cash
130485 
Credit Card
68920 
Prcard
 
431
Mobile
 
74
Pcard
 
65

Length

Max length11
Median length4
Mean length6.4177
Min length4

Characters and Unicode

Total characters1283540
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCash
2nd rowCash
3rd rowCash
4th rowCash
5th rowCash

Common Values

ValueCountFrequency (%)
Cash 130485
65.2%
Credit Card 68920
34.5%
Prcard 431
 
0.2%
Mobile 74
 
< 0.1%
Pcard 65
 
< 0.1%
Split 25
 
< 0.1%

Length

2024-02-25T08:15:39.380608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-25T08:15:39.688517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
cash 130485
48.5%
credit 68920
25.6%
card 68920
25.6%
prcard 431
 
0.2%
mobile 74
 
< 0.1%
pcard 65
 
< 0.1%
split 25
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
C 268325
20.9%
a 199901
15.6%
r 138767
10.8%
d 138336
10.8%
s 130485
10.2%
h 130485
10.2%
i 69019
 
5.4%
e 68994
 
5.4%
t 68945
 
5.4%
68920
 
5.4%
Other values (8) 1363
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 945700
73.7%
Uppercase Letter 268920
 
21.0%
Space Separator 68920
 
5.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 199901
21.1%
r 138767
14.7%
d 138336
14.6%
s 130485
13.8%
h 130485
13.8%
i 69019
 
7.3%
e 68994
 
7.3%
t 68945
 
7.3%
c 496
 
0.1%
l 99
 
< 0.1%
Other values (3) 173
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
C 268325
99.8%
P 496
 
0.2%
M 74
 
< 0.1%
S 25
 
< 0.1%
Space Separator
ValueCountFrequency (%)
68920
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1214620
94.6%
Common 68920
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 268325
22.1%
a 199901
16.5%
r 138767
11.4%
d 138336
11.4%
s 130485
10.7%
h 130485
10.7%
i 69019
 
5.7%
e 68994
 
5.7%
t 68945
 
5.7%
P 496
 
< 0.1%
Other values (7) 867
 
0.1%
Common
ValueCountFrequency (%)
68920
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1283540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 268325
20.9%
a 199901
15.6%
r 138767
10.8%
d 138336
10.8%
s 130485
10.2%
h 130485
10.2%
i 69019
 
5.4%
e 68994
 
5.4%
t 68945
 
5.4%
68920
 
5.4%
Other values (8) 1363
 
0.1%

company
Categorical

MISSING 

Distinct28
Distinct (%)< 0.1%
Missing93668
Missing (%)46.8%
Memory size1.5 MiB
Chicago Carriage Cab Corp
18457 
303 Taxi
17705 
City Service
9989 
Medallion Leasin
9306 
Taxi Affiliation Service Yellow
8937 
Other values (23)
41938 

Length

Max length36
Median length31
Mean length16.397105
Min length8

Characters and Unicode

Total characters1743537
Distinct characters45
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMetro Group
2nd row303 Taxi
3rd row303 Taxi
4th row303 Taxi
5th row303 Taxi

Common Values

ValueCountFrequency (%)
Chicago Carriage Cab Corp 18457
 
9.2%
303 Taxi 17705
 
8.9%
City Service 9989
 
5.0%
Medallion Leasin 9306
 
4.7%
Taxi Affiliation Service Yellow 8937
 
4.5%
Sun Taxi 8625
 
4.3%
Globe Taxi 6387
 
3.2%
Metro Group 5986
 
3.0%
Yellow Cab 3148
 
1.6%
Nova Taxi Affiliation Llc 3003
 
1.5%
Other values (18) 14789
 
7.4%
(Missing) 93668
46.8%

Length

2024-02-25T08:15:39.969807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
taxi 55781
19.2%
cab 24777
 
8.5%
chicago 20632
 
7.1%
service 19388
 
6.7%
carriage 18457
 
6.4%
corp 18457
 
6.4%
303 17705
 
6.1%
affiliation 13872
 
4.8%
yellow 12085
 
4.2%
city 9989
 
3.4%
Other values (34) 79471
27.3%

Most occurring characters

ValueCountFrequency (%)
i 193073
 
11.1%
a 190409
 
10.9%
184282
 
10.6%
e 121771
 
7.0%
o 109079
 
6.3%
r 96355
 
5.5%
C 95243
 
5.5%
l 68656
 
3.9%
T 56715
 
3.3%
x 56715
 
3.3%
Other values (35) 571239
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1231353
70.6%
Uppercase Letter 271025
 
15.5%
Space Separator 184282
 
10.6%
Decimal Number 56877
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 193073
15.7%
a 190409
15.5%
e 121771
9.9%
o 109079
8.9%
r 96355
 
7.8%
l 68656
 
5.6%
x 56715
 
4.6%
c 52476
 
4.3%
n 48770
 
4.0%
t 41784
 
3.4%
Other values (13) 252265
20.5%
Uppercase Letter
ValueCountFrequency (%)
C 95243
35.1%
T 56715
20.9%
S 30208
 
11.1%
A 17595
 
6.5%
M 15579
 
5.7%
G 13136
 
4.8%
L 12485
 
4.6%
Y 12085
 
4.5%
P 5606
 
2.1%
N 4942
 
1.8%
Other values (6) 7431
 
2.7%
Decimal Number
ValueCountFrequency (%)
3 35410
62.3%
0 17705
31.1%
2 1878
 
3.3%
4 1878
 
3.3%
5 6
 
< 0.1%
Space Separator
ValueCountFrequency (%)
184282
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1502378
86.2%
Common 241159
 
13.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 193073
12.9%
a 190409
12.7%
e 121771
 
8.1%
o 109079
 
7.3%
r 96355
 
6.4%
C 95243
 
6.3%
l 68656
 
4.6%
T 56715
 
3.8%
x 56715
 
3.8%
c 52476
 
3.5%
Other values (29) 461886
30.7%
Common
ValueCountFrequency (%)
184282
76.4%
3 35410
 
14.7%
0 17705
 
7.3%
2 1878
 
0.8%
4 1878
 
0.8%
5 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1743537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 193073
 
11.1%
a 190409
 
10.9%
184282
 
10.6%
e 121771
 
7.0%
o 109079
 
6.3%
r 96355
 
5.5%
C 95243
 
5.5%
l 68656
 
3.9%
T 56715
 
3.3%
x 56715
 
3.3%
Other values (35) 571239
32.8%

pickup_latitude
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct313
Distinct (%)0.2%
Missing29002
Missing (%)14.5%
Infinite0
Infinite (%)0.0%
Mean41.910256
Minimum41.660136
Maximum42.016046
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-02-25T08:15:40.267708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum41.660136
5-th percentile41.857184
Q141.880994
median41.899156
Q341.944227
95-th percentile41.980264
Maximum42.016046
Range0.35591044
Interquartile range (IQR)0.06323213

Descriptive statistics

Standard deviation0.04677588
Coefficient of variation (CV)0.0011160963
Kurtosis0.32812152
Mean41.910256
Median Absolute Deviation (MAD)0.02029003
Skewness-0.097765595
Sum7166569.9
Variance0.0021879829
MonotonicityNot monotonic
2024-02-25T08:15:40.582686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.98026432 16272
 
8.1%
41.89960211 14446
 
7.2%
41.88498719 10377
 
5.2%
41.97907082 8874
 
4.4%
41.9442266 8688
 
4.3%
41.88099447 8163
 
4.1%
41.89250778 7072
 
3.5%
41.87886558 6830
 
3.4%
41.92268628 5112
 
2.6%
41.96581197 4942
 
2.5%
Other values (303) 80222
40.1%
(Missing) 29002
 
14.5%
ValueCountFrequency (%)
41.66013605 1
 
< 0.1%
41.66367065 3
 
< 0.1%
41.6738199 3
 
< 0.1%
41.68972991 14
< 0.1%
41.69063335 9
< 0.1%
41.69487897 3
 
< 0.1%
41.70612575 4
 
< 0.1%
41.70658788 11
< 0.1%
41.70731145 4
 
< 0.1%
41.71314861 3
 
< 0.1%
ValueCountFrequency (%)
42.01604649 1
 
< 0.1%
42.01571991 1
 
< 0.1%
42.01569675 35
 
< 0.1%
42.00962288 1033
0.5%
42.00941255 1
 
< 0.1%
42.00761259 18
 
< 0.1%
42.00627886 1
 
< 0.1%
42.00555976 5
 
< 0.1%
42.00476456 3
 
< 0.1%
42.00451749 1
 
< 0.1%

pickup_longitude
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct313
Distinct (%)0.2%
Missing29002
Missing (%)14.5%
Infinite0
Infinite (%)0.0%
Mean-87.684178
Minimum-87.913625
Maximum-87.534903
Zeros0
Zeros (%)0.0%
Negative170998
Negative (%)85.5%
Memory size1.5 MiB
2024-02-25T08:15:40.894153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-87.913625
5-th percentile-87.913625
Q1-87.676356
median-87.633308
Q3-87.625192
95-th percentile-87.618868
Maximum-87.534903
Range0.3787217
Interquartile range (IQR)0.05116385

Descriptive statistics

Standard deviation0.099087132
Coefficient of variation (CV)-0.0011300458
Kurtosis1.067884
Mean-87.684178
Median Absolute Deviation (MAD)0.01443968
Skewness-1.6441395
Sum-14993819
Variance0.0098182598
MonotonicityNot monotonic
2024-02-25T08:15:41.197288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.9136246 16272
 
8.1%
-87.63330804 14446
 
7.2%
-87.62099291 10377
 
5.2%
-87.90303966 8874
 
4.4%
-87.65599818 8688
 
4.3%
-87.63274649 8163
 
4.1%
-87.62621491 7072
 
3.5%
-87.62519214 6830
 
3.4%
-87.64948873 5112
 
2.6%
-87.65587879 4942
 
2.5%
Other values (303) 80222
40.1%
(Missing) 29002
 
14.5%
ValueCountFrequency (%)
-87.9136246 16272
8.1%
-87.90303966 8874
4.4%
-87.90188584 5
 
< 0.1%
-87.8773054 15
 
< 0.1%
-87.84435949 1
 
< 0.1%
-87.84158643 3
 
< 0.1%
-87.81378103 18
 
< 0.1%
-87.80602 86
 
< 0.1%
-87.80453201 113
 
0.1%
-87.79803218 136
 
0.1%
ValueCountFrequency (%)
-87.5349029 4
 
< 0.1%
-87.54093551 3
 
< 0.1%
-87.5514282 31
 
< 0.1%
-87.57005827 9
 
< 0.1%
-87.57271713 9
 
< 0.1%
-87.57278199 43
 
< 0.1%
-87.5823657 1
 
< 0.1%
-87.58314372 195
0.1%
-87.58634832 19
 
< 0.1%
-87.58747926 10
 
< 0.1%

pickup_location
Text

MISSING 

Distinct313
Distinct (%)0.2%
Missing29002
Missing (%)14.5%
Memory size1.5 MiB
2024-02-25T08:15:41.618430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length41
Median length36
Mean length35.827413
Min length32

Characters and Unicode

Total characters6126416
Distinct characters20
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)< 0.1%

Sample

1st rowPOINT (-87.7215590627 41.968069)
2nd rowPOINT (-87.7215590627 41.968069)
3rd rowPOINT (-87.7215590627 41.968069)
4th rowPOINT (-87.7215590627 41.968069)
5th rowPOINT (-87.7215590627 41.968069)
ValueCountFrequency (%)
point 170998
33.3%
41.9802643146 16272
 
3.2%
87.913624596 16272
 
3.2%
87.6333080367 14446
 
2.8%
41.899602111 14446
 
2.8%
87.6209929134 10377
 
2.0%
41.8849871918 10377
 
2.0%
87.9030396611 8874
 
1.7%
41.9790708201 8874
 
1.7%
87.6559981815 8688
 
1.7%
Other values (617) 233370
45.5%
2024-02-25T08:15:42.276680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 568150
 
9.3%
1 523075
 
8.5%
4 482693
 
7.9%
9 465332
 
7.6%
6 453056
 
7.4%
7 426928
 
7.0%
. 341996
 
5.6%
341996
 
5.6%
2 326817
 
5.3%
0 318617
 
5.2%
Other values (10) 1877756
30.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4074440
66.5%
Uppercase Letter 854990
 
14.0%
Other Punctuation 341996
 
5.6%
Space Separator 341996
 
5.6%
Dash Punctuation 170998
 
2.8%
Open Punctuation 170998
 
2.8%
Close Punctuation 170998
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 568150
13.9%
1 523075
12.8%
4 482693
11.8%
9 465332
11.4%
6 453056
11.1%
7 426928
10.5%
2 326817
8.0%
0 318617
7.8%
3 258523
6.3%
5 251249
6.2%
Uppercase Letter
ValueCountFrequency (%)
P 170998
20.0%
O 170998
20.0%
T 170998
20.0%
N 170998
20.0%
I 170998
20.0%
Other Punctuation
ValueCountFrequency (%)
. 341996
100.0%
Space Separator
ValueCountFrequency (%)
341996
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 170998
100.0%
Open Punctuation
ValueCountFrequency (%)
( 170998
100.0%
Close Punctuation
ValueCountFrequency (%)
) 170998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5271426
86.0%
Latin 854990
 
14.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 568150
10.8%
1 523075
9.9%
4 482693
9.2%
9 465332
8.8%
6 453056
8.6%
7 426928
8.1%
. 341996
 
6.5%
341996
 
6.5%
2 326817
 
6.2%
0 318617
 
6.0%
Other values (5) 1022766
19.4%
Latin
ValueCountFrequency (%)
P 170998
20.0%
O 170998
20.0%
T 170998
20.0%
N 170998
20.0%
I 170998
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6126416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 568150
 
9.3%
1 523075
 
8.5%
4 482693
 
7.9%
9 465332
 
7.6%
6 453056
 
7.4%
7 426928
 
7.0%
. 341996
 
5.6%
341996
 
5.6%
2 326817
 
5.3%
0 318617
 
5.2%
Other values (10) 1877756
30.7%

dropoff_latitude
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)< 0.1%
Missing73327
Missing (%)36.7%
Infinite0
Infinite (%)0.0%
Mean41.894141
Minimum41.660136
Maximum42.00915
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-02-25T08:15:42.753794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum41.660136
5-th percentile41.85935
Q141.85935
median41.879255
Q341.909496
95-th percentile41.965812
Maximum42.00915
Range0.34901401
Interquartile range (IQR)0.05014595

Descriptive statistics

Standard deviation0.04060171
Coefficient of variation (CV)0.00096915008
Kurtosis0.58554817
Mean41.894141
Median Absolute Deviation (MAD)0.02034703
Skewness0.084042677
Sum5306856.5
Variance0.0016484989
MonotonicityNot monotonic
2024-02-25T08:15:43.229426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
41.87925508 31775
15.9%
41.89960211 27580
 
13.8%
41.85934972 26501
 
13.3%
41.96581197 13049
 
6.5%
41.92208254 7071
 
3.5%
41.968069 4889
 
2.4%
41.79409025 4739
 
2.4%
41.90949567 3493
 
1.7%
41.94315509 2221
 
1.1%
41.8979839 1594
 
0.8%
Other values (24) 3761
 
1.9%
(Missing) 73327
36.7%
ValueCountFrequency (%)
41.66013605 7
 
< 0.1%
41.70731145 27
 
< 0.1%
41.72818206 35
 
< 0.1%
41.74124273 155
 
0.1%
41.78303418 7
 
< 0.1%
41.79409025 4739
2.4%
41.82016661 2
 
< 0.1%
41.82740025 1
 
< 0.1%
41.83115718 3
 
< 0.1%
41.83380026 1
 
< 0.1%
ValueCountFrequency (%)
42.00915006 6
 
< 0.1%
42.00476456 127
 
0.1%
41.97153938 18
 
< 0.1%
41.97028889 38
 
< 0.1%
41.968069 4889
 
2.4%
41.96581197 13049
6.5%
41.95773557 607
 
0.3%
41.94648976 287
 
0.1%
41.94315509 2221
 
1.1%
41.9428593 378
 
0.2%

dropoff_longitude
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)< 0.1%
Missing73327
Missing (%)36.7%
Infinite0
Infinite (%)0.0%
Mean-87.637959
Minimum-87.759857
Maximum-87.534903
Zeros0
Zeros (%)0.0%
Negative126673
Negative (%)63.3%
Memory size1.5 MiB
2024-02-25T08:15:43.764395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-87.759857
5-th percentile-87.683718
Q1-87.642649
median-87.634156
Q3-87.630964
95-th percentile-87.617358
Maximum-87.534903
Range0.22495412
Interquartile range (IQR)0.0116854

Descriptive statistics

Standard deviation0.023970551
Coefficient of variation (CV)-0.00027351791
Kurtosis5.0617278
Mean-87.637959
Median Absolute Deviation (MAD)0.00849291
Skewness-1.6413831
Sum-11101363
Variance0.00057458732
MonotonicityNot monotonic
2024-02-25T08:15:44.232405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
-87.642649 31775
15.9%
-87.63330804 27580
 
13.8%
-87.61735801 26501
 
13.3%
-87.65587879 13049
 
6.5%
-87.63415609 7071
 
3.5%
-87.72155906 4889
 
2.4%
-87.59231086 4739
 
2.4%
-87.6309636 3493
 
1.7%
-87.64069808 2221
 
1.1%
-87.64149153 1594
 
0.8%
Other values (24) 3761
 
1.9%
(Missing) 73327
36.7%
ValueCountFrequency (%)
-87.75985702 38
 
< 0.1%
-87.7560677 1
 
< 0.1%
-87.73893721 18
 
< 0.1%
-87.72155906 4889
2.4%
-87.71750386 378
 
0.2%
-87.7172201 762
 
0.4%
-87.6945983 43
 
< 0.1%
-87.6937939 7
 
< 0.1%
-87.68971128 74
 
< 0.1%
-87.6837181 607
 
0.3%
ValueCountFrequency (%)
-87.5349029 27
 
< 0.1%
-87.5514282 155
 
0.1%
-87.5826303 7
 
< 0.1%
-87.59231086 4739
 
2.4%
-87.5964756 35
 
< 0.1%
-87.60284764 7
 
< 0.1%
-87.61735801 26501
13.3%
-87.62149921 2
 
< 0.1%
-87.62408895 1
 
< 0.1%
-87.6309636 3493
 
1.7%

dropoff_location
Categorical

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)< 0.1%
Missing73327
Missing (%)36.7%
Memory size1.5 MiB
POINT (-87.642648998 41.8792550844)
31775 
POINT (-87.6333080367 41.899602111)
27580 
POINT (-87.6173580061 41.859349715)
26501 
POINT (-87.6558787862 41.96581197)
13049 
POINT (-87.6341560931 41.922082541)
7071 
Other values (29)
20697 

Length

Max length35
Median length35
Mean length34.704436
Min length32

Characters and Unicode

Total characters4396115
Distinct characters20
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowPOINT (-87.7215590627 41.968069)
2nd rowPOINT (-87.7215590627 41.968069)
3rd rowPOINT (-87.7215590627 41.968069)
4th rowPOINT (-87.7215590627 41.968069)
5th rowPOINT (-87.7215590627 41.968069)

Common Values

ValueCountFrequency (%)
POINT (-87.642648998 41.8792550844) 31775
15.9%
POINT (-87.6333080367 41.899602111) 27580
 
13.8%
POINT (-87.6173580061 41.859349715) 26501
 
13.3%
POINT (-87.6558787862 41.96581197) 13049
 
6.5%
POINT (-87.6341560931 41.922082541) 7071
 
3.5%
POINT (-87.7215590627 41.968069) 4889
 
2.4%
POINT (-87.592310855 41.794090253) 4739
 
2.4%
POINT (-87.630963601 41.9094956686) 3493
 
1.7%
POINT (-87.640698076 41.9431550855) 2221
 
1.1%
POINT (-87.6414915334 41.897983898) 1594
 
0.8%
Other values (24) 3761
 
1.9%
(Missing) 73327
36.7%

Length

2024-02-25T08:15:44.729122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
point 126673
33.3%
87.642648998 31775
 
8.4%
41.8792550844 31775
 
8.4%
87.6333080367 27580
 
7.3%
41.899602111 27580
 
7.3%
87.6173580061 26501
 
7.0%
41.859349715 26501
 
7.0%
87.6558787862 13049
 
3.4%
41.96581197 13049
 
3.4%
87.6341560931 7071
 
1.9%
Other values (59) 48465
 
12.8%

Most occurring characters

ValueCountFrequency (%)
8 452151
 
10.3%
1 364030
 
8.3%
4 318135
 
7.2%
6 304539
 
6.9%
9 303711
 
6.9%
7 302523
 
6.9%
. 253346
 
5.8%
253346
 
5.8%
5 245221
 
5.6%
0 228209
 
5.2%
Other values (10) 1370904
31.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2876039
65.4%
Uppercase Letter 633365
 
14.4%
Other Punctuation 253346
 
5.8%
Space Separator 253346
 
5.8%
Dash Punctuation 126673
 
2.9%
Open Punctuation 126673
 
2.9%
Close Punctuation 126673
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 452151
15.7%
1 364030
12.7%
4 318135
11.1%
6 304539
10.6%
9 303711
10.6%
7 302523
10.5%
5 245221
8.5%
0 228209
7.9%
3 206776
7.2%
2 150744
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
O 126673
20.0%
T 126673
20.0%
N 126673
20.0%
I 126673
20.0%
P 126673
20.0%
Other Punctuation
ValueCountFrequency (%)
. 253346
100.0%
Space Separator
ValueCountFrequency (%)
253346
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 126673
100.0%
Open Punctuation
ValueCountFrequency (%)
( 126673
100.0%
Close Punctuation
ValueCountFrequency (%)
) 126673
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3762750
85.6%
Latin 633365
 
14.4%

Most frequent character per script

Common
ValueCountFrequency (%)
8 452151
12.0%
1 364030
9.7%
4 318135
8.5%
6 304539
8.1%
9 303711
8.1%
7 302523
8.0%
. 253346
 
6.7%
253346
 
6.7%
5 245221
 
6.5%
0 228209
 
6.1%
Other values (5) 737539
19.6%
Latin
ValueCountFrequency (%)
O 126673
20.0%
T 126673
20.0%
N 126673
20.0%
I 126673
20.0%
P 126673
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4396115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 452151
 
10.3%
1 364030
 
8.3%
4 318135
 
7.2%
6 304539
 
6.9%
9 303711
 
6.9%
7 302523
 
6.9%
. 253346
 
5.8%
253346
 
5.8%
5 245221
 
5.6%
0 228209
 
5.2%
Other values (10) 1370904
31.2%

Interactions

2024-02-25T08:15:20.001498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:15.410955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:19.541265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:24.075407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:29.154636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:33.155620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:37.750255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:42.795987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:46.984037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:51.991959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:56.840445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:01.014894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:06.398661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:10.737629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:14.966100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:20.253237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:15.665122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:19.815729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:24.348436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:29.417617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:33.429926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:38.044635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:43.060117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:47.619842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:52.420623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:57.097088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:01.272207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:06.739275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:11.000289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:15.229288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:20.518443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:15.939609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:20.104930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:24.632499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:29.705261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:34.021313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:38.420003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:43.346850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:47.903365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:52.906612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:57.381273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:01.550739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:07.043333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:11.300727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:15.493118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:20.807418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:16.205688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:20.402032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:24.905015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:29.972319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:34.298748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:38.811562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:43.618158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:48.175409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:53.317200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:57.641424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:01.826684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:07.314573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:11.565133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:15.762580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:21.081510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:16.462388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:20.672765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:25.224310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:30.223483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:34.576678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:39.213392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:43.886840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:48.460274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:53.717588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:57.913072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:02.098409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:07.571789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:11.822166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:16.027009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:21.364365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:16.740120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:20.958304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:25.671514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:30.506199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:34.886619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:39.645330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:44.168299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:48.746937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:54.013305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:58.208429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:02.387401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:07.868397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:12.116442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:16.325272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:21.626996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:17.009346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:21.249708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:26.101354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:30.785079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:35.168367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:40.033184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:44.447672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:49.025420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:54.315732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:58.491338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:02.663885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:08.164181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:12.404604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:16.620360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:21.889239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:17.284150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:21.532612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:26.508501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:31.025655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:35.441018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:40.406735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:44.690803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:49.297345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:54.577059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:58.745320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:02.942389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:08.437259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:12.666496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:16.963590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:22.173664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:17.574087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:21.832943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:26.948757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:31.300856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:35.740037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:40.838511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:44.967386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:49.598222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:54.871279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:59.045224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:03.224519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:08.726663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:12.953685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:17.409440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:22.450123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:17.862223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:22.131789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:27.300150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:31.563045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:36.035760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:41.128296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:45.309648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:49.887288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:55.149370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:59.328527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:04.015806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:09.008888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:13.240132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:17.849931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:22.719156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:18.124381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:22.422481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:27.698007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:31.822082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:36.314336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:41.394986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:45.586160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:50.166484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:55.409420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:59.589652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:04.392054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:09.312377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:13.528899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:18.161853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:22.979068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:18.385093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:22.699822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:28.067025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:32.073078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:36.588661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:41.656510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:45.874321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:50.456834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:55.681334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:59.859644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:04.772902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:09.578057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:13.793435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:18.466315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:23.274758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:18.675397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:23.225398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:28.324851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:32.331778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:36.895345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:41.935191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:46.157977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:50.740680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:55.972259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:00.145692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:05.227691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:09.875951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:14.091853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:18.862276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:23.561929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:18.961538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:23.535515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:28.612391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:32.611717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:37.188345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:42.231730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:46.453019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:51.116710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:56.269600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:00.446621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:05.629532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:10.162876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:14.406709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:19.299244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:24.412661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:19.258422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:23.815605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:28.869123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:32.872367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:37.482799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:42.520905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:46.725031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:51.573198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:14:56.561434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:00.731443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:06.028782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:10.478092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:14.696370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-25T08:15:19.720247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-02-25T08:15:45.107295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
companydropoff_census_tractdropoff_community_areadropoff_latitudedropoff_locationdropoff_longitudeextrasfarepayment_typepickup_census_tractpickup_community_areapickup_latitudepickup_longitudetipstollstrip_milestrip_secondstrip_total
company1.000-0.036-0.0370.0390.090-0.0090.1450.2030.135-0.048-0.0590.046-0.0080.0900.0040.0260.0720.205
dropoff_census_tract-0.0361.0000.979-0.9681.0000.6150.1440.1530.0280.2420.242-0.1970.0510.0090.0170.1340.1340.158
dropoff_community_area-0.0370.9791.000-0.9401.0000.5650.1430.0370.0480.2000.347-0.4820.4300.035-0.001-0.0010.0910.060
dropoff_latitude0.039-0.968-0.9401.0001.000-0.658-0.136-0.0130.040-0.199-0.3380.506-0.456-0.0460.0040.024-0.066-0.040
dropoff_location0.0901.0001.0001.0001.000-1.000-0.115-0.1560.061-0.148-0.1920.246-0.135-0.0660.004-0.157-0.126-0.163
dropoff_longitude-0.0090.6150.565-0.658-1.0001.0000.1150.1560.0410.1480.192-0.2460.1350.066-0.0040.1570.1260.163
extras0.1450.1440.143-0.136-0.1150.1151.0000.4790.1040.2590.3880.200-0.3350.2190.0300.4250.4110.559
fare0.2030.1530.037-0.013-0.1560.1560.4791.0000.0000.1290.2700.232-0.3930.2750.0270.8650.8580.971
payment_type0.1350.0280.0480.0400.0610.0410.1040.0001.0000.1010.1320.023-0.0590.8930.0080.1960.1820.361
pickup_census_tract-0.0480.2420.200-0.199-0.1480.1480.2590.1290.1011.0000.901-0.354-0.3840.1380.0200.1400.1450.156
pickup_community_area-0.0590.2420.347-0.338-0.1920.1920.3880.2700.1320.9011.000-0.118-0.2190.1700.0180.2850.2650.304
pickup_latitude0.046-0.197-0.4820.5060.246-0.2460.2000.2320.023-0.354-0.1181.000-0.6560.0580.0140.2700.1990.228
pickup_longitude-0.0080.0510.430-0.456-0.1350.135-0.335-0.393-0.059-0.384-0.219-0.6561.000-0.114-0.016-0.436-0.350-0.393
tips0.0900.0090.035-0.046-0.0660.0660.2190.2750.8930.1380.1700.058-0.1141.0000.0120.2540.2400.415
tolls0.0040.017-0.0010.0040.004-0.0040.0300.0270.0080.0200.0180.014-0.0160.0121.0000.0200.0210.032
trip_miles0.0260.134-0.0010.024-0.1570.1570.4250.8650.1960.1400.2850.270-0.4360.2540.0201.0000.8420.842
trip_seconds0.0720.1340.091-0.066-0.1260.1260.4110.8580.1820.1450.2650.199-0.3500.2400.0210.8421.0000.832
trip_total0.2050.1580.060-0.040-0.1630.1630.5590.9710.3610.1560.3040.228-0.3930.4150.0320.8420.8321.000

Missing values

2024-02-25T08:15:25.035081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-25T08:15:26.213506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-25T08:15:27.907997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

unique_keytrip_start_timestamptrip_end_timestamptrip_secondstrip_milespickup_census_tractdropoff_census_tractpickup_community_areadropoff_community_areafaretipstollsextrastrip_totalpayment_typecompanypickup_latitudepickup_longitudepickup_locationdropoff_latitudedropoff_longitudedropoff_location
0e2afbb4f62fb3865c4d928a39e8a4d1e711ea8da2017-11-03 11:45:00 UTC2017-11-03 11:45:00 UTC365.02.3NaNNaNNaNNaN7.200.0NaN0.07.20CashMetro GroupNaNNaNNaNNaNNaNNaN
1a663e8249660b7a3ae13c9a39378ff495564e8e62016-10-28 20:30:00 UTC2016-10-28 20:30:00 UTC192.01.4NaNNaNNaNNaN5.600.0NaN0.05.60Cash303 TaxiNaNNaNNaNNaNNaNNaN
2a99a7309aea3cf70eed1644c254eb30b150ac4f22016-10-28 20:45:00 UTC2016-10-28 20:45:00 UTC328.02.1NaNNaNNaNNaN7.600.0NaN0.07.60Cash303 TaxiNaNNaNNaNNaNNaNNaN
3ad0d9e702d67b0e9e7b85dd0750605ff06389c4f2016-10-28 21:45:00 UTC2016-10-28 22:00:00 UTC706.07.6NaNNaNNaNNaN20.400.0NaN2.022.40Cash303 TaxiNaNNaNNaNNaNNaNNaN
4350b48036f17d07f79abf53d04b811da8b6c264c2016-10-29 09:45:00 UTC2016-10-29 10:30:00 UTC3407.050.7NaNNaNNaNNaN0.010.0NaN0.00.01Cash303 TaxiNaNNaNNaNNaNNaNNaN
5b5d0ec1472abae045f794a581f42364213ef79ab2016-10-29 11:30:00 UTC2016-10-29 11:30:00 UTC123.00.2NaNNaNNaNNaN3.000.0NaN0.03.00Cash303 TaxiNaNNaNNaNNaNNaNNaN
6ac7ce885bf43a27e0e22de0c1e8efd98ebdd15712016-10-29 12:00:00 UTC2016-10-29 12:15:00 UTC419.02.4NaNNaNNaNNaN7.400.0NaN1.08.40Cash303 TaxiNaNNaNNaNNaNNaNNaN
768088a5a378c45781ece9cdb1eebdc2afc4047d32017-11-03 12:30:00 UTC2017-11-03 13:15:00 UTC2069.03.8NaNNaNNaNNaN20.200.0NaN0.020.20CashMetro GroupNaNNaNNaNNaNNaNNaN
84f4d0f4eec17eae0c01aa5a62facf62ccbb48e572017-11-03 13:15:00 UTC2017-11-03 13:30:00 UTC460.02.8NaNNaNNaNNaN8.200.0NaN0.08.20CashMetro GroupNaNNaNNaNNaNNaNNaN
9b695916c866d9137a22ad63bb26668d7bdb462c22017-11-03 15:15:00 UTC2017-11-03 15:15:00 UTC3.00.0NaNNaNNaNNaN2.000.0NaN0.02.00CashMetro GroupNaNNaNNaNNaNNaNNaN
unique_keytrip_start_timestamptrip_end_timestamptrip_secondstrip_milespickup_census_tractdropoff_census_tractpickup_community_areadropoff_community_areafaretipstollsextrastrip_totalpayment_typecompanypickup_latitudepickup_longitudepickup_locationdropoff_latitudedropoff_longitudedropoff_location
19999087d5788c78dfb92ee8ed9c31908cb2124b064f1d2014-04-26 18:30:00 UTC2014-04-26 18:45:00 UTC1560.010.8NaNNaN8.041.024.650.000.00.024.65CashNaN41.899602-87.633308POINT (-87.6333080367 41.899602111)41.79409-87.592311POINT (-87.592310855 41.794090253)
19999167e3eb3d78a65c2b913ecd473fd05c677dfd47382014-05-12 20:45:00 UTC2014-05-12 21:15:00 UTC1320.016.5NaNNaN8.041.035.250.000.00.035.25CashNaN41.899602-87.633308POINT (-87.6333080367 41.899602111)41.79409-87.592311POINT (-87.592310855 41.794090253)
1999928b7001ad7784574ebe32b9f3f0d8d8ccd712c0b32014-05-11 01:45:00 UTC2014-05-11 02:15:00 UTC1200.08.9NaNNaN8.041.021.054.410.01.026.46Credit CardNaN41.899602-87.633308POINT (-87.6333080367 41.899602111)41.79409-87.592311POINT (-87.592310855 41.794090253)
1999933e23d3638d307b27df4afd31bba5b31ab535a85a2014-04-22 21:15:00 UTC2014-04-22 21:30:00 UTC1380.08.5NaNNaN8.041.021.250.000.02.023.25CashNaN41.899602-87.633308POINT (-87.6333080367 41.899602111)41.79409-87.592311POINT (-87.592310855 41.794090253)
19999404191a501246d7a82e88bdfb217dabf7beb218192014-04-25 22:30:00 UTC2014-04-25 22:45:00 UTC1020.08.1NaNNaN8.041.019.050.000.00.019.05CashNaN41.899602-87.633308POINT (-87.6333080367 41.899602111)41.79409-87.592311POINT (-87.592310855 41.794090253)
1999957c5eee2d5cfa2efbded24be40fad831ca0e0c9832014-05-07 10:15:00 UTC2014-05-07 10:30:00 UTC1020.09.8NaNNaN8.041.022.250.000.00.022.25CashNaN41.899602-87.633308POINT (-87.6333080367 41.899602111)41.79409-87.592311POINT (-87.592310855 41.794090253)
199996f8d6826d7b5b1e5f33a8fe6f397d0650c4d4e5152014-05-23 11:00:00 UTC2014-05-23 11:30:00 UTC1500.08.7NaNNaN8.041.022.251.500.00.023.75Credit CardNaN41.899602-87.633308POINT (-87.6333080367 41.899602111)41.79409-87.592311POINT (-87.592310855 41.794090253)
19999774c5df50d09536ef33bc928e5d20a422a015e0c52014-05-03 03:45:00 UTC2014-05-03 04:00:00 UTC1200.07.9NaNNaN8.041.018.853.770.00.022.62Credit CardNaN41.899602-87.633308POINT (-87.6333080367 41.899602111)41.79409-87.592311POINT (-87.592310855 41.794090253)
199998ece539fddea592c9a9929013d9b37e6463156ac52014-06-07 03:30:00 UTC2014-06-07 03:45:00 UTC1080.010.1NaNNaN8.041.022.453.000.00.025.45Credit CardNaN41.899602-87.633308POINT (-87.6333080367 41.899602111)41.79409-87.592311POINT (-87.592310855 41.794090253)
199999bb053bfdb1dd7024c75e9cad526201dda0b755232014-05-14 20:30:00 UTC2014-05-14 20:45:00 UTC1080.08.2NaNNaN8.041.019.450.000.00.019.45CashNaN41.899602-87.633308POINT (-87.6333080367 41.899602111)41.79409-87.592311POINT (-87.592310855 41.794090253)